Outcomes of advance care directives after admission to a long-term care home: DNR the DNH?

Background Residents of long-term care homes (LTCH) often experience unnecessary and non-beneficial hospitalizations and interventions near the end-of-life. Advance care directives aim to ensure that end-of-life care respects resident needs and wishes. Methods In this retrospective cohort study, we used multistate models to examine the health trajectories associated with Do-Not-Resuscitate (DNR) and Do-Not-Hospitalize (DNH) directives of residents admitted to LTCH in Ontario, Alberta, and British Columbia, Canada. We adjusted for baseline frailty-related health instability. We considered three possible end states: change in health, hospitalization, or death. For measurements, we used standardized RAI-MDS 2.0 LTCH assessments linked to hospital records from 2010 to 2015. Results We report on 123,003 LTCH residents. The prevalence of DNR and DNH directives was 71 and 26% respectively. Both directives were associated with increased odds of transitioning to a state of greater health instability and death, and decreased odds of hospitalization. The odds of hospitalization in the presence of a DNH directive were lowered, but not eliminated, with odds of 0.67 (95% confidence interval 0.65–0.69), 0.63 (0.61–0.65), and 0.47 (0.43–0.52) for residents with low, moderate and high health instability, respectively. Conclusion Even though both DNR and DNH orders are associated with serious health outcomes, DNH directives were not frequently used and often overturned. We suggest that policies recommending DNH directives be re-evaluated, with greater emphasis on advance care planning that better reflects resident values and wishes.

A New Multistate Transition Model For Effect Estimation In Randomized Trials With Treatment Switching And A Cured Subgroup

Background: Many methods, including multistate models, have been proposed in the literature to estimate the treatment effect on overall survival in randomized trials with treatment switching permit after the disease progression. Nevertheless, the cured fraction of patients has not been considered. The cured would never experience the progressive disease, but they may suffer death with a hazard comparable to that of people without the disease. With the mix of the cured subgroup, existing methods yield highly biased effect estimation and fail to reflect the truth in uncured patients. Methods: In this paper, we propose a new multistate transition model to incorporate the cure, progression, treatment switching, and death states during trials. In the proposed model, the probability of cure and the death hazard of the cured are modeled separately. For the not cured patients, the semi-competing risks model is used with the treatment effect evaluated via transitional hazards between states. The particle swarm optimization algorithm is adopted to estimate the model parameters. Results: Extensive simulation studies have been conducted to evaluate the performance of the proposed multistate model and compare it with existing treatment switching adjustment methods. Results show that in all scenarios, the treatment effect estimation of the proposed model is more accurate than that of existing treatment switching adjustment methods. Besides, the application to diffuse large B-cell lymphoma data has also illustrated the superiority of the proposed model.Conclusions: The superiority and robustness of the proposed multistate transition model qualify it to estimate the treatment effect in trials with the treatment switching permit after progression and a cured subgroup.

Estimation of Life Expectancy for Dependent Population in a Multi-State Context

Population statistics show that there was an increase in life expectancy during the last century. However, this fact hides that this increase was not equal for all groups of the population. One of the most problematic cases for measuring this increase is that of the dependent population because of the absence of specific statistics. This paper describes a methodology for calculating life expectancy using multistate models that take into account the diversity of situations considered by Spanish legislation. For this purpose, statistical information contained in the national survey on disability and dependency (EDAD 2008) is used. The results suggest that life expectancies are lower than those of the general population and that they differ according to gender and intensity of suffering from this contingency. The calculations were made considering the legal framework currently existing in Spain. This fact conditions the definition of dependent person and, therefore, the set of individuals, their characteristics, and therefore, their final results.

Multistate Models for the Analysis of Time to Type II Chronic Diabetic Complications in Debre Markos Referral Hospital, Northwest Ethiopia

Introduction: Diabetes is a chronic, non-communicable disease characterized by elevated blood glucose levels. The purpose of this study was to jointly model the transition of diabetic patients in a series of clinical states and to assess the relationship between each state and different patient characteristics. Methods: A hospital-based retrospective study was conducted on 524 patients with type II diabetes, aged 18 years or older, who attended their medication between January 1, 2005, and December 31, 2017. Multistate models with different assumptions were considered to explore the effects of different prognostic factors on the transition intensity of type II diabetes mellitus patients. Results: During a median follow-up time of 7.4 years (Inter-Quartile Range=4.01), 54.8% of diabetic patients developed either microvascular or macrovascular complications, and 10.5% of them experienced both microand macrocomplications, and 16.66% of diabetes patients died. The assumption Markov was assessed by using the likelihood ratio test showed that Markov assumption was not held just for the transition. The transition rate of patients from the macrovascular state to the death state was affected by the residence of the patients (P=0.05) and age at diagnosis (p=0.01). The transition rates of patients with microvascular complications to death were significantly affected by baseline triglyceride level (P<0.001), age at first diagnosis (P=0.01), baseline glucose level (P=0.03, and baseline serum creatinine level (P=0.04). Conclusion: The semi-Markov model fitted the data well and could be used as a convenient model for the analysis of time to diabetes-related complications or death.

Survival Analysis of the Human Immunodeficiency Virus in Iranian Patients: A Multistate Model

Background and Aim: Acquired Immunodeficiency Syndrome (AIDS) caused by Human Immunodeficiency Virus (HIV), is a chronic and potentially life-threatening disease. Numerous factors affect its development and progression. Therefore, the present study attempted to identify characteristics impacting the prognosis and progression of AIDS using multistate models. Methods & Materials: The present retrospective study consisted of 2185 patients affected with HIV referring to Behavioral Disease Counseling Centers in Tehran City, Iran, from 2004 to 2013. We considered multiple states of AIDS, tuberculosis, and tuberculosis/AIDS in the natural history of the disease (from the onset of HIV disease until death occurred). Then, we applied the multistate models, to examine the effect of contextual demographic and clinical variables on survival time; subsequently, the transition probabilities of HIV. Ethical Considerations: This study was approved by the Research Ethics Committee of Hamadan University of Medical Sciences (Code: IR.UMSHA.REC.1396.117). Results: HIV-Related deaths in individuals with an incarnation history were 2.40 times higher than in those without the prison history. Death risk was also 1.70 and 1.80 times higher in those aged 25-44 and 44 years, respectively, compared to the individuals aged less than 25 years. An inverse relationship was also found between CD4 levels and the risk of death in our participants. Conclusion: Antiretroviral therapy, CD4 count, age, and history of imprisonment were the main factors in the progression of the disease and subsequent death in HIV patients. Thus, preventing the further spread of the disease to the community and controlling the disease in the patients requires targeted educational and therapeutic interventions; accordingly, the community will be familiarized with transmission routes and the preventing principle of disease. Furthermore, we can encourage patients to visit the healthcare centers early.

Trends in disability-free life expectancy at age 50 years in Australia between 2001 and 2011 by social disadvantage

BackgroundThe aims of this study were (1) to estimate 10-year trends in disability-free life expectancy (DFLE) by area-level social disadvantage and (2) to examine how incidence, recovery and mortality transitions contributed to these trends.MethodsData were drawn from the nationally representative Household Income and Labour Dynamics in Australia survey. Two cohorts (baseline age 50+ years) were followed up for 7 years, from 2001 to 2007 and from 2011 to 2017, respectively. Social disadvantage was indicated by the Socio-Economic Indexes for Areas (SEIFA). Two DFLEs based on a Global Activity Limitation Indicator (GALI) and difficulties with activities of daily living (ADLs) measured by the 36-Item Short Form Survey physical function subscale were estimated by cohort, sex and SEIFA tertile using multistate models.ResultsPersons residing in the low-advantage tertile had more years lived with GALI and ADL disability than those in high-advantage tertiles. Across the two cohorts, dynamic equilibrium for GALI disability was observed among men in mid-advantage and high-advantage tertiles, but expansion of GALI disability occurred in the low-advantage tertile. There was expansion of GALI disability for all women irrespective of their SEIFA tertile. Compression of ADL disability was observed for all men and for women in the high-advantage tertile. Compared to the 2001 cohort, disability incidence was lower for the 2011 cohort of men within mid-advantage and high-advantage tertiles, whereas recovery and disability-related mortality were lower for the 2011 cohort of women within the mid-advantage tertile.ConclusionOverall, compression of morbidity was more common in high-advantage areas, whereas expansion of morbidity was characteristic of low-advantage areas. Trends also varied by sex and disability severity.

A review of multistate modelling approaches in monitoring disease progression: Bayesian estimation using the Kolmogorov-Chapman forward equations

There are numerous fields of science in which multistate models are used, including biomedical research and health economics. In biomedical studies, these stochastic continuous-time models are used to describe the time-to-event life history of an individual through a flexible framework for longitudinal data. The multistate framework can describe more than one possible time-to-event outcome for a single individual. The standard estimation quantities in multistate models are transition probabilities and transition rates which can be mapped through the Kolmogorov-Chapman forward equations from the Bayesian estimation perspective. Most multistate models assume the Markov property and time homogeneity; however, if these assumptions are violated, an extension to non-Markovian and time-varying transition rates is possible. This manuscript extends reviews in various types of multistate models, assumptions, methods of estimation and data features compatible with fitting multistate models. We highlight the contrast between the frequentist (maximum likelihood estimation) and the Bayesian estimation approaches in the multistate modeling framework and point out where the latter is advantageous. A partially observed and aggregated dataset from the Zimbabwe national ART program was used to illustrate the use of Kolmogorov-Chapman forward equations. The transition rates from a three-stage reversible multistate model based on viral load measurements in WinBUGS were reported.

Role of depressive symptoms in cardiometabolic diseases and subsequent transitions to all-cause mortality: an application of multistate models in a prospective cohort study

Background and purposeThe role of depression in the development and outcome of cardiometabolic diseases remains to be clarified. We aimed to examine the extent to which depressive symptoms affect the transitions from healthy to diabetes, stroke, heart disease and subsequent all-cause mortality in a middle-aged and elderly European population.MethodsA total of 78 212 individuals aged ≥50 years from the Survey of Health Ageing and Retirement in Europe were included. Participants with any baseline cardiometabolic diseases including diabetes, stroke and heart disease were excluded. Depressive symptoms were measured by the Euro-Depression scale at baseline. Participants were followed up to determine the occurrence of cardiometabolic diseases and all-cause mortality. We used multistate models to estimate the transition-specific HRs and 95% CIs after adjustment of confounders.ResultsDuring 500 711 person-years of follow-up, 4742 participants developed diabetes, 2173 had stroke, 5487 developed heart disease and 7182 died. Depressive symptoms were significantly associated with transitions from healthy to diabetes (HR: 1.12, 95% CI: 1.05 to 1.20), stroke (HR: 1.31, 95% CI: 1.18 to 1.44), heart disease (HR: 1.26, 95% CI: 1.18 to 1.34) and all-cause mortality (HR: 1.41, 95% CI: 1.34 to 1.49). After cardiometabolic diseases, depressive symptoms were associated with the increased risk of all-cause mortality in patients with diabetes (HR: 1.54, 95% CI: 1.25 to 1.89), patients who had stroke (HR: 1.29, 95% CI: 1.03 to 1.61) and patients with heart disease (HR: 1.21, 95% CI: 1.02 to 1.44).ConclusionsDepressive symptoms increase the risk of diabetes, stroke and heart disease, and affect the risk of mortality after the onset of these cardiometabolic conditions. Screening and treatment of depressive symptoms may have profound implications for the prevention and prognosis of cardiometabolic diseases.